Keywords: COMPETENCY BASED TRAINING;FRAMEWORK;LEARNING ANALYTICS;READINESS

Additional Keywords not in the list above: Linked Data, Digitization, competency, data standards, metadata training, sustainment.

Learning Objective 1: Recognize information with digitization value in legacy training materials.

Learning Objective 2: Understand the differences and relationships between digital frameworks, taxonomies, competencies, and labels.

Learning Objective 3: At a high level, understand the strategy for leveraging linked data to store competency frameworks and compute learner profiles.

Learning Objective 4: Develop competency framework and taxonomy import structure from legacy training materials.

Learning Objective 5: Understand some data security techniques applied during the competency digitization process.

Abstract:

Efficient and dynamic sustainment of force-wide training and readiness requires an organization to build and maintain digital representations of comprehensive personnel job requirements, training materials, performance criteria and expectations. These digital representations are used during the planning, development and tracking of both training and job performance, enabling real-time readiness computations and data-driven training effectiveness evaluations. Although the U.S. military has created numerous systems that seek to collect and maintain human performance data, a significant gap remains in the lack of shared digital expressions of readiness across the ecosystem. In this tutorial, we review and demonstrate techniques for implementing centralized, technical standards-based competency and skills frameworks using existing training materials, job descriptions, and performance criteria that can be leveraged for actionable insights across the full training and readiness lifecycle. Our “digitization” approach streamlines the framework construction process and has been implemented over numerous recent Navy, Army, and Air Force projects to convert legacy artifacts that describe human experience, capability, potential, and expectations into normalized digital frameworks that support analysis and tracking in a modern learning ecosystem. We demonstrate how our semi-automated digitization techniques could be applied to all available data that defines what personnel should know and do within a military organization, including (1) who a person is and their training background; (2) the job duties associated with a person’s role and assignments; (3) what a person has demonstrated they know and can do; (4) what credentials a person has earned; and (5) what a person's capabilities and goals are according to their organization. We use a civilian/military medical simulation use case to illustrate how competency frameworks have been and would be employed for team and individual performance analysis, skills gap analysis, training needs analysis, instructional design, assessment, and evaluation. This tutorial provides insights into machine actionable URI-referenceable data known as Linked Data, discusses approaches for applying appropriate security measures based on the information it represents, and reviews how the use of linked data has allowed systems to perform rapid analysis by uniformly following data trails across the system, organizational, and authoritative boundaries. The tutorial concludes with a discussion of how taking steps to “digitize” knowledge, skills, tasks and duties can provide organizations with a foundation focused on the human element of training and readiness, enabling a data-driven accelerator for sustaining a global force in a digital world.

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Mrs. Evelyn Rowland